Project Group 30!¶

Members:

  1. Rody Boting
  2. Hugo Odijk
  3. Tim de Ridder
  4. Wouter van der Veer
  5. Jesse Zegeling

Student numbers:

  1. 5421446
  2. 4945824
  3. 4937961
  4. 4708121
  5. 4717309

Research Objective¶

Requires data modeling and quantitative research in Transport, Infrastructure & Logistics

What were the main air traffic delays for domestic flights in the USA over 2015?¶

Subquestions¶

  • What are the main causes of delay?
  • Which airports have the most delay?
  • To which airline are the most delays linked?
  • How do delays change over the year/week per airport?
  • Is there a correlation between weather and delay?

Hypothesis¶

  1. De grote van de luchthaven is gelijk aan de grote van de vertraging.
  2. De verwachting is dat het weer meer invloed op de vertraging dan andere redenen van vertraging.

Contribution Statement¶

Be specific. Some of the tasks can be coding (expect everyone to do this), background research, conceptualisation, visualisation, data analysis, data modelling

To be determined:

Author 1 (Rody): Coding, communication

Author 2 (Hugo): Coding

Author 3 (Tim): Coding

Author 4 (Wouter): Coding

Author 5 (Jesse): Coding

Data Used¶

  • Flight data USA in 2015:

    • https://www.kaggle.com/datasets/usdot/flight-delays

  • Weather data:

    • https://github.com/fivethirtyeight/data/tree/master/us-weather-history

  • IATA-codes list, including coordinates:

    • https://raw.githubusercontent.com/lxndrblz/Airports/main/airports.csv
    • iata-utils/iata_airlines.csv at master · benct/iata-utils (github.com)

  • Passenger numbers of airports USA in 2015 (Will not be used in the project, out of scope):

    • https://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/passenger/media/cy15-commercial-service-enplanements.pdf

Data Pipeline¶

The large dataset of flights and delays, including the concerning airports and which airlines operate the flights can be used to visualise where in America the delays took place in the year 2015. This can be animated per day using colours and lines that change over the year. To clearly display the visualisations, there is also a data set with coordinates of the airports and their full names.
Finally, daily data from rainfall is also compared to the delays to see if there is a trend between the two.

Process:

  1. Data import
  2. Data processing
  3. Data visualisation

Data import¶

In [1]:
%matplotlib notebook


from urllib.request import urlopen
from plotly.offline import init_notebook_mode
import plotly.express as px
import plotly.graph_objects as go
import json
import pandas as pd
import numpy as np
import itertools
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import plotly.io as pio
import geopandas as gpd
import math
from plotly.subplots import make_subplots

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [2]:
df = pd.read_csv('flights.csv', parse_dates={"date":['YEAR', 'MONTH', 'DAY']})
dM = pd.read_csv('flights.csv', parse_dates={"date":['YEAR', 'MONTH']})

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\IPython\core\interactiveshell.py:3457: DtypeWarning:

Columns (7,8) have mixed types.Specify dtype option on import or set low_memory=False.

Data processing¶

In [3]:
df['date'] = df['date'].astype(str)
df["count"] = np.ones(len(df))
dM['date'] = dM['date'].astype(str)
dM["count"] = np.ones(len(dM))

#Arrivel delay for airline,
AL = df.groupby(["date", "AIRLINE"]).sum().reset_index()
AL["ArvCount"] = AL["ARRIVAL_DELAY"] / AL["count"]
AL2 = dM.groupby(["date", "AIRLINE"]).sum().reset_index()
AL2["ArvCount"] = AL2["ARRIVAL_DELAY"] / AL2["count"]

# Departure delay for airport
AP = dM.groupby(["date", "ORIGIN_AIRPORT"]).sum().reset_index()
AP["DepCount"] = AP["DEPARTURE_DELAY"] / AP["count"]
In [4]:
# Filling the gaps - Month
Month_unique = dM.date.unique() #Setting required inputs
Airline_unique = dM.AIRLINE.unique() #Setting required inputs
a = []
b = []
for i in range(len(Month_unique)):
    for j in range(len(Airline_unique)):
        a.append(Month_unique[i])
        b.append(Airline_unique[j])
c = np.zeros(len(a)) #All non-existing values will be 0, as they will not be overruled
data = {'Date': a,
        'Airline': b,
       'Average arrival delay': c}
Months_df = pd.DataFrame(data)


for k in range(len(Months_df.Date)):
    for l in range(len(AL2.date)):
        if Months_df.Date[k]==AL2.date[l] and Months_df.Airline[k]==AL2.AIRLINE[l]:
            Months_df["Average arrival delay"][k] = AL2.ArvCount[l]

# To slow for all days
# Days_unique = df.date.unique() #Setting required inputs
# Airline_unique = df.AIRLINE.unique() #Setting required inputs
# a = []
# b = []
# for i in range(len(Days_unique)):
#     for j in range(len(Airline_unique)):
#         a.append(Days_unique[i])
#         b.append(Airline_unique[j])
# c = np.zeros(len(a)) #All non-existing values will be 0, as they will not be overruled
# data = {'Date': a,
#         'Airline': b,
#        'Average arrival delay': c}
# Days_df = pd.DataFrame(data)


# for k in range(len(Days_df.Date)):
#     for l in range(len(AL.date)):
#         if Days_df.Date[k]==AL.date[l] and Days_df.Airline[k]==AL.AIRLINE[l]:
#             Days_df["Average arrival delay"][k] = AL.ArvCount[l]            

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\ipykernel_launcher.py:20: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [5]:
fig = px.bar(Months_df,y="Airline", x="Average arrival delay",animation_frame="Date",animation_group="Airline",color="Airline", range_x=[-20,100])
fig.update_yaxes(categoryorder="total ascending")
fig.update_layout(showlegend=False,
    title="Average arrival delay of airlines",
    xaxis_title="Average arrival delay [min/flight]",
    yaxis_title="Airlines",)
fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000
fig.show()
In [6]:
df = pd.read_csv('flights.csv')

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\IPython\core\interactiveshell.py:3457: DtypeWarning:

Columns (7,8) have mixed types.Specify dtype option on import or set low_memory=False.
In [7]:
airports = df.ORIGIN_AIRPORT.unique()

### Choose Airport ###
Airport = "ATL"
fly = []
delay = []
for i in range(len(df)):
    if df.ORIGIN_AIRPORT[i] == Airport:
        fly.append(df.DESTINATION_AIRPORT[i])
        if df.ARRIVAL_DELAY[i]>0:
            delay.append(df.ARRIVAL_DELAY[i])
        else:
            delay.append(0)
    if df.DESTINATION_AIRPORT[i] == Airport:
        fly.append(df.ORIGIN_AIRPORT[i])
        if df.ARRIVAL_DELAY[i]>0:
            delay.append(df.ARRIVAL_DELAY[i])
        else:
            delay.append(0)
In [8]:
Destinations = np.unique(fly)
df_flights = pd.DataFrame(index = Destinations)
Count = np.zeros(len(Destinations))
Delay = np.zeros(len(Destinations))
df_flights["cnt"] = Count
df_flights["Airport"] = Destinations
df_flights["Delay"] = Delay
In [9]:
for i in range(len(fly)):
    for j in range(len(df_flights)):
        if fly[i] == df_flights.iloc[j,1]:
            df_flights.iloc[j,0] += 1
            df_flights.iloc[j,2] += delay[i]
display(df_flights)
cnt Airport Delay
ABE 1802.0 ABE 20399.0
ABQ 1604.0 ABQ 15851.0
ABY 1752.0 ABY 23443.0
ACY 357.0 ACY 7197.0
AEX 2466.0 AEX 38170.0
... ... ... ...
TVC 66.0 TVC 586.0
TYS 3437.0 TYS 31852.0
VLD 1870.0 VLD 21826.0
VPS 4415.0 VPS 42826.0
XNA 3151.0 XNA 33355.0

170 rows × 3 columns

In [10]:
df_airports = pd.read_csv('airports.csv')
df_airports
Out[10]:
IATA_CODE AIRPORT CITY STATE COUNTRY LATITUDE LONGITUDE
0 ABE Lehigh Valley International Airport Allentown PA USA 40.65236 -75.44040
1 ABI Abilene Regional Airport Abilene TX USA 32.41132 -99.68190
2 ABQ Albuquerque International Sunport Albuquerque NM USA 35.04022 -106.60919
3 ABR Aberdeen Regional Airport Aberdeen SD USA 45.44906 -98.42183
4 ABY Southwest Georgia Regional Airport Albany GA USA 31.53552 -84.19447
... ... ... ... ... ... ... ...
317 WRG Wrangell Airport Wrangell AK USA 56.48433 -132.36982
318 WYS Westerly State Airport West Yellowstone MT USA 44.68840 -111.11764
319 XNA Northwest Arkansas Regional Airport Fayetteville/Springdale/Rogers AR USA 36.28187 -94.30681
320 YAK Yakutat Airport Yakutat AK USA 59.50336 -139.66023
321 YUM Yuma International Airport Yuma AZ USA 32.65658 -114.60597

322 rows × 7 columns

In [11]:
df_flights = df_flights.rename(columns={'Airport': 'IATA'})

### Add empty columns to the DataFrame ###
df_flights['start'] = Airport
df_flights['airport'] = ''
df_flights['end_lat'] = ''
df_flights['end_lon'] = ''
df_flights['start_lat'] = ''
df_flights['start_lon'] = ''

### Add values to the columns ###
for i in range(len(df_flights)):
    for j in range(len(df_airports)):
        if df_flights['IATA'][i] == df_airports['IATA_CODE'][j]:
            df_flights['airport'][i] = df_airports['AIRPORT'][j]
            df_flights['end_lat'][i] = df_airports['LATITUDE'][j]
            df_flights['end_lon'][i] = df_airports['LONGITUDE'][j]
        if df_flights['start'][i] == df_airports['IATA_CODE'][j]:            
            df_flights['start_lat'][i] = df_airports['LATITUDE'][j]
            df_flights['start_lon'][i] = df_airports['LONGITUDE'][j]
        

display(df_flights)

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\ipykernel_launcher.py:15: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\ipykernel_launcher.py:16: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\ipykernel_launcher.py:17: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\ipykernel_launcher.py:19: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\ipykernel_launcher.py:20: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
cnt IATA Delay start airport end_lat end_lon start_lat start_lon
ABE 1802.0 ABE 20399.0 ATL Lehigh Valley International Airport 40.65236 -75.4404 33.64044 -84.42694
ABQ 1604.0 ABQ 15851.0 ATL Albuquerque International Sunport 35.04022 -106.60919 33.64044 -84.42694
ABY 1752.0 ABY 23443.0 ATL Southwest Georgia Regional Airport 31.53552 -84.19447 33.64044 -84.42694
ACY 357.0 ACY 7197.0 ATL Atlantic City International Airport 39.45758 -74.57717 33.64044 -84.42694
AEX 2466.0 AEX 38170.0 ATL Alexandria International Airport 31.32737 -92.54856 33.64044 -84.42694
... ... ... ... ... ... ... ... ... ...
TVC 66.0 TVC 586.0 ATL Cherry Capital Airport 44.74144 -85.58224 33.64044 -84.42694
TYS 3437.0 TYS 31852.0 ATL McGhee Tyson Airport 35.81249 -83.99286 33.64044 -84.42694
VLD 1870.0 VLD 21826.0 ATL Valdosta Regional Airport 30.7825 -83.27672 33.64044 -84.42694
VPS 4415.0 VPS 42826.0 ATL Destin-Fort Walton Beach Airport/Eglin AFB 30.48325 -86.5254 33.64044 -84.42694
XNA 3151.0 XNA 33355.0 ATL Northwest Arkansas Regional Airport 36.28187 -94.30681 33.64044 -84.42694

170 rows × 9 columns

In [14]:
df_flights["avg_delay"] = df_flights["Delay"] / df_flights["cnt"]
In [15]:
fig = go.Figure()

fig.add_trace(go.Scattergeo(
    locationmode = 'USA-states',
    lon = df_airports['LONGITUDE'],
    lat = df_airports['LATITUDE'],
    hoverinfo = 'text',
    text = df_airports['AIRPORT'],
    mode = 'markers',
    marker = dict(
        size = 2,
        color = 'rgb(255, 100, 100)',
        line = dict(
            width = 3,
            color = 'rgba(100, 100, 100, 0)'
        )
    )))

flight_paths = []
for i in range(len(df_flights)):
    fig.add_trace(
        go.Scattergeo(
            locationmode = 'USA-states',
            lon = [df_flights['start_lon'][i], df_flights['end_lon'][i]],
            lat = [df_flights['start_lat'][i], df_flights['end_lat'][i]],
            mode = 'lines',
            line = dict(width = 1,color = 'red'),
            opacity = float(df_flights['cnt'][i]) / float(df_flights['cnt'].max()),
        )
    )

fig.update_layout(
    title_text = f'All flights from {Airport} in 2015',
    showlegend = False,
    geo = dict(
        scope = 'north america',
        projection_type = 'natural earth',
        showland = True,
        landcolor = 'rgb(200, 200, 200)',
        countrycolor = 'rgb(180, 180, 180)',
    ),
)

fig.show()
In [16]:
fig = go.Figure()

fig.add_trace(go.Scattergeo(
    locationmode = 'USA-states',
    lon = df_airports['LONGITUDE'],
    lat = df_airports['LATITUDE'],
    hoverinfo = 'text',
    text = df_airports['AIRPORT'],
    mode = 'markers',
    marker = dict(
        size = 2,
        color = 'rgb(255, 100, 100)',
        line = dict(
            width = 3,
            color = 'rgba(100, 100, 100, 0)'
        )
    )))

flight_paths = []
for i in range(len(df_flights)):
    fig.add_trace(
        go.Scattergeo(
            locationmode = 'USA-states',
            lon = [df_flights['start_lon'][i], df_flights['end_lon'][i]],
            lat = [df_flights['start_lat'][i], df_flights['end_lat'][i]],
            mode = 'lines',
            line = dict(width = 1,color = 'red'),
            opacity = float(df_flights['avg_delay'][i]) / float(df_flights['avg_delay'].max()),
        )
    )

fig.update_layout(
    title_text = f'Average delay of flights from {Airport} in 2015',
    showlegend = False,
    geo = dict(
        scope = 'north america',
        projection_type = 'natural earth',
        showland = True,
        landcolor = 'rgb(200, 200, 200)',
        countrycolor = 'rgb(180, 180, 180)',
    ),
)

fig.show()
In [17]:
df = pd.read_csv('flights.csv')

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\IPython\core\interactiveshell.py:3457: DtypeWarning:

Columns (7,8) have mixed types.Specify dtype option on import or set low_memory=False.
In [18]:
airports = df.ORIGIN_AIRPORT.unique()

origin = []
destination = []
for i in range(len(df)):
    if df.ARRIVAL_DELAY[i] >= 24*60:
        origin.append(df.ORIGIN_AIRPORT[i])
        destination.append(df.DESTINATION_AIRPORT[i])
In [19]:
df_flights = pd.DataFrame(index = origin)
df_flights["Origin"] = origin
df_flights["Destination"] = destination
In [20]:
df_airports = pd.read_csv('airports.csv')
df_airports
Out[20]:
IATA_CODE AIRPORT CITY STATE COUNTRY LATITUDE LONGITUDE
0 ABE Lehigh Valley International Airport Allentown PA USA 40.65236 -75.44040
1 ABI Abilene Regional Airport Abilene TX USA 32.41132 -99.68190
2 ABQ Albuquerque International Sunport Albuquerque NM USA 35.04022 -106.60919
3 ABR Aberdeen Regional Airport Aberdeen SD USA 45.44906 -98.42183
4 ABY Southwest Georgia Regional Airport Albany GA USA 31.53552 -84.19447
... ... ... ... ... ... ... ...
317 WRG Wrangell Airport Wrangell AK USA 56.48433 -132.36982
318 WYS Westerly State Airport West Yellowstone MT USA 44.68840 -111.11764
319 XNA Northwest Arkansas Regional Airport Fayetteville/Springdale/Rogers AR USA 36.28187 -94.30681
320 YAK Yakutat Airport Yakutat AK USA 59.50336 -139.66023
321 YUM Yuma International Airport Yuma AZ USA 32.65658 -114.60597

322 rows × 7 columns

In [21]:
df_flights = df_flights.rename(columns={'Origin': 'IATA1'})
df_flights = df_flights.rename(columns={'Destination': 'IATA2'})

### Add empty columns to the DataFrame ###
df_flights['end_lat'] = ''
df_flights['end_lon'] = ''
df_flights['start_lat'] = ''
df_flights['start_lon'] = ''

### Add values to the columns ###
for i in range(len(df_flights)):
    for j in range(len(df_airports)):
        if df_flights.iloc[i,1] == df_airports['IATA_CODE'][j]:
            df_flights['end_lat'][i] = df_airports['LATITUDE'][j]
            df_flights['end_lon'][i] = df_airports['LONGITUDE'][j]
        if df_flights.iloc[i,0] == df_airports['IATA_CODE'][j]:            
            df_flights['start_lat'][i] = df_airports['LATITUDE'][j]
            df_flights['start_lon'][i] = df_airports['LONGITUDE'][j]

display(df_flights)
IATA1 IATA2 end_lat end_lon start_lat start_lon
AUS AUS DFW 32.89595 -97.0372 30.19453 -97.66987
LAS LAS LAX 33.94254 -118.40807 36.08036 -115.15233
JAX JAX DFW 32.89595 -97.0372 30.49406 -81.68786
BHM BHM DFW 32.89595 -97.0372 33.56294 -86.75355
FAT FAT DFW 32.89595 -97.0372 36.77619 -119.71814
JFK JFK HNL 21.31869 -157.92241 40.63975 -73.77893
EGE EGE ORD 41.9796 -87.90446 39.64257 -106.9177
STL STL MIA 25.79325 -80.29056 38.74769 -90.35999
HNL HNL LAX 33.94254 -118.40807 21.31869 -157.92241
SAT SAT DFW 32.89595 -97.0372 29.53369 -98.46978
OMA OMA DFW 32.89595 -97.0372 41.30252 -95.89417
SMF SMF DFW 32.89595 -97.0372 38.69542 -121.59077
FLL FLL ORD 41.9796 -87.90446 26.07258 -80.15275
SAN SAN JFK 40.63975 -73.77893 32.73356 -117.18966
PNS PNS DFW 32.89595 -97.0372 30.47331 -87.18744
LAX LAX STL 38.74769 -90.35999 33.94254 -118.40807
SMF SMF DFW 32.89595 -97.0372 38.69542 -121.59077
SFO SFO DFW 32.89595 -97.0372 37.619 -122.37484
RIC RIC DFW 32.89595 -97.0372 37.50517 -77.31967
IND IND LAX 33.94254 -118.40807 39.71733 -86.29438
RIC RIC DFW 32.89595 -97.0372 37.50517 -77.31967
HNL HNL LAX 33.94254 -118.40807 21.31869 -157.92241
SAN SAN DFW 32.89595 -97.0372 32.73356 -117.18966
STX STX MIA 25.79325 -80.29056 17.70189 -64.79856
14747 14747 11298
11413 11413 11298
11612 11612 13930
MCI MCI DFW 32.89595 -97.0372 39.29761 -94.71391
MCO MCO JFK 40.63975 -73.77893 28.42889 -81.31603
DTW DTW ORD 41.9796 -87.90446 42.21206 -83.34884
MSP MSP ORD 41.9796 -87.90446 44.88055 -93.21692
ABQ ABQ DFW 32.89595 -97.0372 35.04022 -106.60919
In [22]:
fig = go.Figure()

fig.add_trace(go.Scattergeo(
    locationmode = 'USA-states',
    lon = df_airports['LONGITUDE'],
    lat = df_airports['LATITUDE'],
    hoverinfo = 'text',
    text = df_airports['AIRPORT'],
    mode = 'markers',
    marker = dict(
        size = 2,
        color = 'rgb(255, 100, 100)',
        line = dict(
            width = 3,
            color = 'rgba(100, 100, 100, 0)'
        )
    )))

flight_paths = []
for i in range(len(df_flights)):
    fig.add_trace(
        go.Scattergeo(
            locationmode = 'USA-states',
            lon = [df_flights.iloc[i,5], df_flights.iloc[i,3]],
            lat = [df_flights.iloc[i,4], df_flights.iloc[i,2]],
            mode = 'lines',
            line = dict(width = 1,color = 'red'),
            opacity = 1,
        )
    )

fig.update_layout(
    title_text = f'Flights in 2015 with more than 24 hour delay',
    showlegend = False,
    geo = dict(
        scope = 'north america',
        projection_type = 'natural earth',
        showland = True,
        landcolor = 'rgb(200, 200, 200)',
        countrycolor = 'rgb(180, 180, 180)',
    ),
)

fig.show()
In [23]:
# pip install openpyxl
In [24]:
df = pd.read_csv('flights.csv', parse_dates={"date":['YEAR', 'MONTH', 'DAY']})
df3 = pd.read_excel('Weather data.xlsx', index_col='airport')

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\IPython\core\interactiveshell.py:3457: DtypeWarning:

Columns (7,8) have mixed types.Specify dtype option on import or set low_memory=False.
In [25]:
df_KHOU = df3.loc['KHOU']
# Plot of KHOU

fig = go.Figure()
fig.add_trace(go.Bar(x=df_KHOU.date, y=df_KHOU.actual_precipitation,
                    name='Actual precipitation'))
fig.add_trace(go.Scatter(x=df_KHOU[df_KHOU.actual_precipitation > 20].date, y=df_KHOU[df_KHOU.actual_precipitation > 20].actual_precipitation,
                    mode='markers', name='Precipitation above 20 inch'))

fig.update_layout(xaxis=dict(title="Date"), yaxis=dict(title="Actual precipitation [inches]"))

fig.update_layout(
    title_text="Actual precipitation KHOU")

fig.show()
In [26]:
# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Bar(x=df_KHOU.date, y=df_KHOU.actual_precipitation, name='Actual precipitation'),
    secondary_y=False)

fig.add_trace(go.Scatter(
    x=df_KHOU[df_KHOU.actual_precipitation > 20].date, y=df_KHOU[df_KHOU.actual_precipitation > 20].actual_precipitation,
                    mode='markers', name='Precipitation above 20 inch'))

fig.add_trace(go.Scatter(x=df_KHOU.date, y=df_KHOU.actual_mean_temp, name="Actual mean temp"),
    secondary_y=True)

# Add figure title
fig.update_layout(
    title_text="Temperature and precipitation KHOU"
)

# Set x-axis title
fig.update_xaxes(title_text="Date")

# Set y-axes titles
fig.update_yaxes(title_text="Precipitation [Inches]", secondary_y=False)
fig.update_yaxes(title_text="Temperature [F]", secondary_y=True)

fig.show()
In [27]:
Airport = "HOU"
df['date'] = df['date'].astype(str)
delay = []
for i in range(len(df)):
    if df.ORIGIN_AIRPORT[i] == Airport:
        if df.ARRIVAL_DELAY[i] > 0:
            delay.append([df.date[i], df.ARRIVAL_DELAY[i]])
        else:
            delay.append([df.date[i], 0])
In [28]:
dates = df['date'].unique()
df2 = pd.DataFrame(index = dates)

df2['Total_Delay'] = 0
df2['Flights'] = 0

for i in range(len(df2)):
    for j in range(len(delay)):
        if df2.index[i] == delay[j][0]:
            df2.Total_Delay[i] += delay[j][1]
            df2.Flights[i] += 1
            
df2['Average_Delay'] = df2['Total_Delay'] / df2['Flights']
In [29]:
df2['Rainfall'] = ' '
for i in range(len(df_KHOU)):
    df2.Rainfall[i] = df_KHOU.actual_precipitation[i]
df4 = df2.iloc[0:len(df_KHOU)]

C:\Users\thade\Miniconda3\envs\TIL6022\lib\site-packages\ipykernel_launcher.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [33]:
fig = plt.figure()

ax = fig.add_subplot(111)
ax2 = ax.twinx()

df4.Rainfall.plot(kind='bar', color='red', ax=ax, position=1)
df4.Average_Delay.plot(kind='bar', color='blue', ax=ax2, position=0)

ax.set_ylabel('Rainfall [inches]')
ax2.set_ylabel('Average Delay [minutes]')

for i in range(len(df_airports)):
    if Airport == df_airports['IATA_CODE'][i]:
        Airport_name = df_airports['AIRPORT'][i]
    
plt.title(f'Delay and rainfall per day at {Airport_name} in 2015 ')

n=10
ticks = ax.xaxis.get_ticklocs()
ticklabels = ax.xaxis.get_ticklabels()

ax.xaxis.set_ticks(ticks[::n])
ax.xaxis.set_ticklabels(ticklabels[::n])

ax.legend(loc=1)
ax2.legend(loc=2)

plt.xlim(50,150)
plt.show()

Data visualisation¶

In [ ]:
#CODE

Analysis of results¶

To be determined

Conclusion & discussion¶

To be determined

In [ ]:
#END